Learning Counterfactually Fair Models via Improved Generation with Neural Causal Models

Understand

One of the main concerns while deploying machine learning models in real-world applications is fairness.

• Counterfactual fairness has emerged as an intuitive and natural definition of fairness.
• However, existing methodologies for enforcing counterfactual fairness seem to have two limitations: (i) generating counterfactual samples faithful to the underlying causal graph, and (ii) as we argue in this paper, existing regularizers are mere proxies and do not directly enforce the exact definition of counterfactual fairness.
• In this work, our aim is to mitigate both issues.